Observation-based training of artificial intelligence character models

ABSTRACT

Systems and methods for observation-based training of an Artificial Intelligence (AI) character model are provided. An example method includes receiving log data including interactions of a first user and a second user and adjusting, based on the log data, parameters of the AI character model to cause the AI character model to mimic behavioral characteristics of the first user in follow-up conversations with further users.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority of U.S. Provisional PatentApplication No. 63/335,929 filed on Apr. 28, 2022, entitled“OBSERVATION-BASED TRAINING OF ARTIFICIAL INTELLIGENCE CHARACTERMODELS.” The subject matter of aforementioned application isincorporated herein by reference in its entirety for all purposes.

TECHNICAL FIELD

This disclosure generally relates to artificial intelligence (AI)-basedcharacter models. More particularly, this disclosure relates toobservation-based training of AI character models.

BACKGROUND

Virtual characters are widely used in various software applications,such as games, metaverses, social media, messengers, video communicationtools, and online training tools. Some of these applications allow usersto interact with virtual characters. However, existing models of virtualcharacters are typically developed for specific applications and do notallow integration with other applications and environments. Moreover,existing virtual character models are typically based on descriptions ofspecific rules and logic.

Parameters of conventional virtual character models typically remainunchanged for the whole time of interaction between the users and thevirtual characters. This approach results in virtual character modelsthat lack the ability to modify their behavioral characteristics.Specifically, it is hard to change the behavior of the conventionalvirtual characters to mimic a desired behavior that some virtualcharacters or users previously had. Accordingly, tools are needed thatwould allow virtual character models to train to change theirinteractions with users based on observing interactions between usersand virtual characters.

SUMMARY

This section is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription section. This summary is not intended to identify keyfeatures or essential features of the claimed subject matter, nor is itintended to be used as an aid in determining the scope of the claimedsubject matter.

In one example embodiment, a computing platform for observation-basedtraining of an AI character model is provided. The computing platformmay include a processor and a memory storing instructions to be executedby the processor. The computing platform may be configured to receivelog data that include interactions of a first user and a second user.The computing platform may be further configured to adjust, based on thelog data, parameters of the AI character model to cause the AI charactermodel to mimic behavioral characteristics of the first user in follow-upconversations with further users.

In another example embodiment, a method for observation-based trainingof an AI character model is provided. The method may be implemented witha processor of a computing platform for observation-based training of anAI character model. The method may commence with receiving, by theprocessor, log data including interactions of a first user and a seconduser. Upon receiving the log data, the method may proceed withadjusting, by the processor and based on the log data, parameters of theAI character model to cause the AI character model to mimic behavioralcharacteristics of the first user in follow-up conversations withfurther users.

According to another example embodiment, provided is a non-transitorycomputer-readable storage medium having instructions stored thereon,which, when executed by one or more processors, cause the one or moreprocessors to perform steps of the method for observation-based trainingof an AI character model.

Additional objects, advantages, and novel features of the examples willbe set forth in part in the description which follows, and in part willbecome apparent to those skilled in the art upon examination of thefollowing description and the accompanying drawings or may be learned byproduction or operation of the examples. The objects and advantages ofthe concepts may be realized and attained by means of the methodologies,instrumentalities and combinations particularly pointed out in theappended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments are illustrated by way of example and not limitation in thefigures of the accompanying drawings, in which like references indicatesimilar elements.

FIG. 1 illustrates an environment within which systems and methods forobservation-based training of an AI character model can be implemented.

FIG. 2 is a block diagram illustrating a platform for generating an AIcharacter model, according to an example embodiment.

FIG. 3 provides additional details for an AI character model, inaccordance with an example embodiment.

FIG. 4 is an architecture diagram that shows using a surroundingarchitecture of an AI character model to control an output and behaviorgenerated by large language models (LLMs), according to an exampleembodiment.

FIG. 5 is a detailed architecture diagram showing a surroundingarchitecture of an AI character model, according to an exampleembodiment.

FIG. 6A is a detailed architecture diagram showing a surroundingarchitecture of an AI character model, according to an exampleembodiment.

FIG. 6B is a detailed architecture diagram showing a surroundingarchitecture of an AI character model, according to an exampleembodiment.

FIG. 7A shows an architecture diagram illustrating AI character modelswith goal-oriented behavior, according to an example embodiment.

FIG. 7B shows an architecture diagram illustrating AI character modelswith goal-oriented behavior, according to an example embodiment.

FIG. 8 is a block diagram illustrating a narrative structure that showsa context of scenes used to distinguish context for goals, according toan example embodiment.

FIG. 9 is a block diagram illustrating a structure of goals withinscenes, according to an example embodiment.

FIG. 10 illustrates a method for observation-based training of an AIcharacter model, according to an example embodiment.

FIG. 11 is a high-level block diagram illustrating an example computersystem, within which a set of instructions for causing the machine toperform any one or more of the methodologies discussed herein can beexecuted.

DETAILED DESCRIPTION

The following detailed description of embodiments includes references tothe accompanying drawings, which form a part of the detaileddescription. Approaches described in this section are not prior art tothe claims and are not admitted to be prior art by inclusion in thissection. The drawings show illustrations in accordance with exampleembodiments. These example embodiments, which are also referred toherein as “examples,” are described in enough detail to enable thoseskilled in the art to practice the present subject matter. Theembodiments can be combined, other embodiments can be utilized, orstructural, logical, and operational changes can be made withoutdeparting from the scope of what is claimed. The following detaileddescription is, therefore, not to be taken in a limiting sense, and thescope is defined by the appended claims and their equivalents.

The approaches described in this section could be pursued but are notnecessarily approaches that have previously been conceived or pursued.Therefore, unless otherwise indicated, it should not be assumed that anyof the approaches described in this section qualify as prior art merelyby virtue of their inclusion in this section.

Reference will now be made in detail to embodiments, examples of whichare illustrated in the accompanying drawings. In the following detaileddescription, numerous specific details are set forth in order to providea thorough understanding of the present disclosure. However, it will beapparent to one of ordinary skill in the art that the present disclosuremay be practiced without these specific details. In other instances,well-known methods, procedures, components, and circuits have not beendescribed in detail so as not to unnecessarily obscure aspects of theembodiments.

Embodiments of the present disclosure are directed to a platform forgenerating AI character models and performing interactions between theAI character models and users. In one example embodiment, the platformmay receive a description of a character and generate an AI charactermodel capable of interacting with users verbally and through emotions,gestures, actions, and movements. The description can be provided asnatural language describing a role, motivation, and environment of an AIcharacter. The platform may utilize a common knowledge concerning the AIcharacter to train the AI character model in order to interact with theusers. The AI character model may evolve its characteristics, changeemotions, and acquire knowledge based on conversations with the users.

The AI character model may utilize a LLM in conversations with theusers. In order to obtain more effective and appropriate responses touser questions and messages, the platform may apply variousrestrictions, classifications, shortcuts, and filters in response touser questions. These targeted requests to the LLMs will result inoptimized performance. For example, prior to sending a request to theLLM, the platform may classify and filter the user questions andmessages to change words based on the personalities of AI characters,emotional states of AI characters, emotional states of users, context ofa conversation, scene and environment of the conversation, and so forth.Similarly, the platform may adjust the response formed by the LLM bychanging words and adding fillers based on the personality, role, andemotional state of the AI character. The AI character model may changeemotions based on the role of the AI character and in response toemotions of the user.

The platform may include integration interfaces, such as applicationprogramming interfaces (APIs), allowing external applications to use theAI character model. The AI character models generated by the platformcan be used in game applications, virtual events and conversations,corporate training, and so on.

Some embodiments of the present disclosure relate to a system and amethod for observation-based training of an AI character model. Thesystem and the method may be integrated into the platform for generatingAI character models. The AI character model may be presented to the userin the form of an AI character in a virtual environment provided to theuser via a client-side computing device.

The observation-based training of an AI character model is referred toas a role-based training of the AI character model. The role-basedtraining of the AI character model may result in learning by the AIcharacter model to generate an AI character that has parameters of anactor that played a role of a character. The system may use apre-trained AI character model implemented as a pre-trained neuralnetwork, receive log data, and adjust the parameters of the AI charactermodel based on the log data. The neural network may have a plurality ofnodes and connections. The adjusting of the parameters of the AIcharacter model may include updating parameters associated with thenodes and the connections based on the log data to predict with higherprobability the instances of behavioral characteristics that aredesirable or that mimic the log data.

The adjustment of the parameters may include learning by the AIcharacter model by observing the log data. For example, the system mayread a log of conversations between a first user and a second user andthe description of a scene in a virtual environment. In the interactionsbetween the first user and the second user, the second user may play aspecific role (for example, a barista). Upon reading the log data, thesystem may generate, based on the log data and the description of thescene, the parameters of an AI character that is a virtual barista.

Thus, the system may provide a fine-tuned AI character model that ismore specific to the log data provided to the AI character model.Accordingly, the AI character model trained by the system may provide AIcharacters that mimic behavioral characteristics observed by the AIcharacter model based on the log data.

Referring now to the drawings, FIG. 1 illustrates an environment 100within which systems and methods for observation-based training of an AIcharacter model can be implemented. The environment 100 may include aclient-side computing device 102 associated with a user 104, a computingplatform 106 for providing an AI character model (also referred toherein as a computing platform 106), and a data network shown as anetwork 108. The computing platform 106 and client-side computing device102 (also referred to herein as a client) may communicate via thenetwork 108.

The client-side computing device 102 may include, but is not limited to,a smartphone, a laptop, a personal computer, a desktop computer, atablet computer, a phablet, a personal digital assistant, a mobiletelephone, a smart television set, a personal computing device, and thelike. The computing platform 106 may include a processor 110 and amemory 112 storing instructions to be executed by the processor 110.

The network 108 can refer to any wired, wireless, or optical networksincluding, for example, the Internet, intranet, a Local Area Network(LAN), a Personal Area Network, Wide Area Network (WAN), a VirtualPrivate Network, a Wi-Fi® network, cellular phone networks (e.g., aGlobal System for Mobile (GSM) communications network, a packetswitching communications network, a circuit switching communicationsnetwork), Bluetooth™ radio, an Ethernet network, an IEEE 802.11-basedradio frequency network, a Frame Relay network, an Internet Protocol(IP) communications network, or any other data communication networkutilizing physical layers, link layer capability, or network layers tocarry data packets, or any combinations of the above-listed datanetworks. In some embodiments, the network 108 may include a corporatenetwork, a data center network, a service provider network, a mobileoperator network, or any combinations thereof.

The computing platform 106 may be associated with an AI character model(shown in detail in FIG. 2 ). The AI character model may be configuredto generate AI-based characters, also referred herein to as AIcharacters. The user 104 may use the computing platform 106 to createthe AI character models and interact with the AI character models viathe client-side computing device 102 in a virtual environment associatedwith the AI character. The virtual environment can be generated by theclient-side computing device 102 for presenting to the user 104. Thecomputing platform 106 is shown in detail in FIG. 2 as a platform 200.

FIG. 2 illustrates a platform 200 for generating AI character models,according to an example embodiment. The platform 200 may include astudio 204, an integration interface 206, and an AI character model 202.AI character models are also referred to herein as AI-based charactermodels. The studio 204 and the integration interface 206 may be incommunication with data sources 226. The data sources 226 may includeonline search services. The data sources 226 may include a set ofclusters each associated with a type of a feature of an AI character.

In one example embodiment, the studio 204 may receive, via a userinterface, a character description 208 of an AI character. The studio204 may generate, based on the character description 208, an AIcharacter model 202 corresponding to the AI character. An AI characteris also referred to herein as an AI-based character.

The character description 208 can be provided using a natural humanlanguage. The character description may include a description of an AIcharacter similar to a description of a character to be played that canbe provided to a real actor. The user interface of the studio 204 mayinclude input fields allowing a developer to enter different aspects(i.e., parameters) of the AI character. Each input field may define apart of the brain of the AI character.

The input fields may include a text field for entering a coredescription of the AI character. An example core description can include“Buddy is a kind young man from Argentina.” The input fields may includea text field for entering a motivation of the AI character. An examplemotivation may include “Buddy likes to dance.”

The input fields may also include a text field for entering commonknowledge and facts that the AI character may possess. For example, thefield for common knowledge may include “orcs from Mordor; orcs like toeat hobbits.”

The input fields may include fields for selecting an avatar and voice ofthe AI character. The input fields may include fields for definingmemory and personality features of the AI character. The input fieldsmay also include a text field describing the scene and environment inwhich the AI character is placed. For example, the text field for thescene may include “savanna,” “city,” “forest,” “bar,” and so forth.

The integration interface 206 may receive a user input 210, environmentparameters 212, and events 214 and generate, based on the AI charactermodel 202, a model output 216.

The user input 210 may include voice messages of a user. The voicemessages may include phrases commonly used in conversations. Theintegration interface 206 may generate, based on the voice messages,requests and provide the request to the AI character model 202 togenerate the model output 216. In an example embodiment, the requestsmay include text messages verbalized by the user and an emotional stateof the user.

The model output 216 may include verbal messages 218, gestures 220,emotions 222, and movements 224. The verbal messages 218 may includeresponses to the voice messages of the user. The gestures 220 mayinclude specific hand and facial movements of the AI character, eitheraccompanying the verbal messages 218 or occurring without the verbalmessages 218. Gestures 220 may include, for example, waving goodbye,nodding to indicate agreement, or pointing to indicate a direction.Gestures 220 are typically intentional and have a specific meaning thatis understood by those familiar with the culture or context in whichthey are used. Emotions 222 may include intonations of the voice of theAI character while uttering the verbal messages 218 or facialexpressions of the AI character. Movements 224 may refer to the overallmovements and postures of the body of the AI character, including theposition of the arms, legs, and torso. The movements 224 can be used toconvey a range of emotions and attitudes, such as confidence,relaxation, or nervousness. Movements 224 can be both intentional andunintentional.

FIG. 3 provides additional details of an AI character model 300, inaccordance with an example embodiment. The AI character model 300 mayinclude a set of models including an avatar 302, a language model 304, agesture model 306, an emotional model 308, a behavioral model 310, andthe like. The models may include machine learning models. In someembodiments, the models can be implemented as artificial neuralnetworks. The AI character model 300 can include runtime parameters 312and design parameters 314.

The design parameters 314 may correspond to settings for personality andgeneral emotions of an AI character. The design parameters 314 can begenerated based on character description 208 received via the studio 204shown in FIG. 2 .

The runtime parameters 312 may correspond to an emotional state of an AIcharacter. The emotional state can be changed based on conversationswith the user, elements in the scene, the surrounding environment inwhich the AI character is currently present, and so forth.

The avatar 302 may include a three-dimensional body model rendering theAI character. In some embodiments, the avatar 302 can be created usingapplications currently available on the market.

The language model 304 can be based on a LLM. The LLM is a machinelearning algorithm that can recognize, predict, and generate humanlanguages on the basis of very large text-based data sets. The languagemodel 304 may form a request for the LLM, receive a response from theLLM, and process the response from the LLM to form a response to voicemessages of the user. The request for the LLM can include classificationand adjustment of the text requests from the integration interface 206,according to the current scene, environmental parameters, an emotionalstate of the AI character, an emotional state of the user, and currentcontext of the conversation with the user. Processing of the responsefrom the LLM may include filtering of the response to exclude unwantedwords, verifying relevancy of the response, changing the words in theresponse, and adding fillers to phrases according to the personality ofAI characters. In other embodiments, the language model 304 may alsoretrieve data from available sources, such as Wikipedia® or GameWikipedia®, to generate the response.

The gesture model 306 may generate a movement of the body of the AIcharacter based on the response to the user, an emotional state of theAI character, and current scene parameters. For example, the AIcharacter may turn to the user and raise a hand in response to agreeting from the user. The greeting gestures can differ based on scenesand environments.

The emotional model 308 may track the emotional state of the AIcharacter based on the context of the conversation with the user, anemotional state of the user, a scene and environmental parameters, andso forth.

The behavioral model 310 may track and change behavioral characteristicsof the AI character as a result of conversations with users or changesin the environment and scenes during a predetermined time period.

In general, the LLM can statistically suggest a continuation to anyinput provided to the LLM. If a conversation is started by using theLLM, the LLM may propose the next step for the conversation. Forexample, if a conversation includes a story related to some topic, theLLM may propose the next line for the story.

One of the key characteristics of LLMs is the fact that LLMs are large.In particular, the LLMs are trained on vast amounts of data. When usedin conversations, the LLMs can statistically suggest some textdetermined by the LLMs to be meaningful in the next step of theconversation. Therefore, the LLMs conventionally build the conversationbased on the text itself.

FIG. 4 is an architecture diagram 400 that shows using a surroundingarchitecture of an AI character model to control an output and behaviorgenerated by LLMs, according to an example embodiment. The main stepsimplemented to control the output and behavior of AI characters usingthe AI character model include an input step 402 (step A), atransformation step 404 (step B), an orchestration step 406 (step C),and a generation step 408 (step D). The input step 402 includesproviding a connection with a client and performing input streaming. Thetransformation step 404 includes pre-processing and transforming anincoming data stream. The orchestration step 406 and the generation step408 include processing and transforming an incoming data stream. StepsA-D are shown in detail in FIG. 5 , FIG. 6A, and FIG. 6B.

FIG. 5 is a detailed architecture diagram 500 showing a surroundingarchitecture of an AI character model, according to an exampleembodiment. The input step (step A) may include establishing aconnection between a client and a server, as shown in block 502. In anexample embodiment, the client may include a user device associated witha user. The user may use the client device to interact with AIcharacters in a virtual environment using an application running on theuser device. To establish the connection between the system of thepresent disclosure and the client, a server (e.g., a web server), a gameclient, and an application running on the user device may be provided.The server, the game client, and the application may be set up based onpredetermined rules to enable streaming multimodal inputs from theclient to the server, as shown in block 504. The inputs are shown indetail in FIG. 6A.

FIG. 6A and FIG. 6B show a detailed architecture diagram 600 thatillustrates a surrounding architecture of an AI character model,according to an example embodiment. The connection established betweenthe client and the server via predetermined protocols enables collectinga plurality of streams of inputs from the client. Each stream may beassociated with one of multiple modalities. In an example embodiment,the modality may include a type of data. As shown in FIG. 6A, the inputscollected from the client may include text 602, audio 604, visuals 606,events 608, actions 610, gestures (not shown), and so forth.

Referring again to FIG. 5 , the transformation step (step B) may includepre-processing the incoming streams of data in block 506. The streams ofinputs may be pre-processed differentially based on the specificmodality. The pre-processing may include converting the received inputsinto a singular format. The pre-processing is shown in detail in FIG.6A.

As shown in FIG. 6A, the text 602 is in the form of a natural languageand may need no pre-processing. The audio 604 may be pre-processed usinga speech to text conversion 612, in the course of which the audio inputmay be transformed into text. The visuals 606 may be pre-processed usinga machine vision 614 based on object classification, environmentunderstanding, and so forth.

The events 608 may include any event received from the client. Anexample event may include a button click in a game, an AI charactermoving a sword in a game, a button click in a web application, and soforth. The actions 610 may be received from an environment of AIcharacters with which the user interacts. An example action may includereacting to a horse riding by in an application, calling a web hook toretrieve information, and so forth. The events 608 and the actions 610may be processed into client triggers 616. Based on the pre-processing,all inputs may be transformed into text and/or embeddings 618. Theembeddings (also referred to as word embeddings) are wordrepresentations, in which words with similar meaning have a similarrepresentation. Thus, a pre-processed data stream in the form of textand/or embeddings 618 may be obtained upon pre-processing of thereceived inputs.

Referring again to FIG. 5 , the transformation step (step B) may furtherinclude running the pre-processed data through a series of machinelearning models that represent different elements of cognition andproducing intermediate outputs, as shown in block 508. Processing thedata using the series of machine learning models is shown in detail inFIG. 6A.

As shown in FIG. 6A, the text and/or embeddings 618 may be passedthrough a plurality of machine learning models shown as heuristicsmodels 620. The processing of the text and/or embeddings 618 using theheuristics models 620 may include passing the text and/or embeddings 618through a goals model 622, a safety model 624, an intent recognitionmodel 626, an emotion model 628, an events model 630, and a plurality offurther heuristics models 632.

The goals model 622 may be configured to process the text and/orembeddings 618 and recognize, based on what was said by the user or theAI character, what goals need to be activated. The safety model 624 maybe configured to process the text and/or embeddings 618 and filter outunsafe responses. The intent recognition model 626 may be configured toprocess the text and/or embeddings 618 and determine what a player(i.e., a user) intends to do and use an intent to trigger one or moreevents at a later point of interaction of the player with AI charactersin the game.

The emotion model 628 may be configured to process the text and/orembeddings 618 and update, based on what the player said, the emotionsof the AI character. The events model 630 may be configured to processthe text and/or embeddings 618 and determine the events. The events mayact as triggers for performing an action based on predetermined rules.For example, a predetermined rule may include a rule according to whichwhen the player steps into a specific location (the event) near the AIcharacter, the AI character takes a predetermined action.

Upon the processing of the data, the heuristics models 620 may provideintermediate outputs. Each of the intermediate outputs provided by theheuristics models 620 may be a differential element. Specifically, thegoals model 622, the safety model 624, the intent recognition model 626,the emotion model 628, and the events model 630 may each provide aspecific sort of a separate element. The separate elements need to beorchestrated by composing together into a specific templated format.

Referring again to FIG. 5 , the orchestration step (step C) may includecomposing the intermediate outputs received from the heuristics modelsinto templated formats for ingestion by LLMs and animation, gesture, andaction models in block 510. Upon composing the intermediate outputs intoa template, the composed outputs may be fed into primary modelsrepresenting elements of multimodal expression, as shown in block 512.The orchestration step (step C) is further shown in detail in FIG. 6B.

As shown in FIG. 6B, the orchestration step (step C) may includeformatting and representation 634 of the intermediate outputs receivedfrom the heuristics models. Upon being formatted, the composed data maybe sent to another series of AI models. Specifically, the composed datareceived in block 510 shown in FIG. 5 may include dialogue prompts 636,active goals and actions 638 (i.e., what goals and actions need to beactive based on what was said or done by the user or the AI character),animation and gesture state 640 (i.e., what gestures or animations needto be active depending on the emotional state and the goal), narrativetriggers 642, voice parameters 644, and so forth. The dialogue prompts636 may be provided to a LLM 646. The active goals and actions 638 maybe provided to a goals and actions model 648, the narrative controller650, and the animation and gesture model 652. The animation and gesturestate 640 may be provided to the goals and actions model 648, thenarrative controller 650, and the animation and gesture model 652.

The narrative triggers 642 may be provided to the goals and actionsmodel 648, the narrative controller 650, and the animation and gesturemodel 652. An example of the narrative triggers 642 may include words “Iwant to be in the investigation” said by the player. The goals andactions model 648, the narrative controller 650, and/or the animationand gesture model 652 may receive this narrative trigger and change thestoryline and progress forward in the game.

The voice parameters 644 may be used for enacting the voice in thevirtual environment. For example, if the AI character is angry, thevoice parameter “angry” may be used to change the voice of the AIcharacter in the game. If the state of the AI character changes to veryforceful, the state can be shown by changing the voice of the AIcharacter.

Referring again to FIG. 5 , the generation step (step D) may includeusing primary models and systems to generate final behavior-aligned dataoutputs in block 514. The generation step (step D) may further includestreaming outputs through predetermined protocols to the client andapplying final transformations in block 516. The generation step (stepD) is further shown in detail in FIG. 6B.

As shown in FIG. 6B, the LLM 646 is a model used to generate a dialogueoutput 654. The goals and actions model 648 and the narrative controller650 both decide what needs to be sent to the client side. The clientside may be represented by a client engine, a game engine, a webapplication running on a client-side computing device, and the like. Thegoals and actions model 648 and the narrative controller 650 may decidewhat needs to be enacted on the client side. The animation and gesturemodel 652 may decide what animations or gestures need to be activated onthe client side to enact the behavior of AI characters. Therefore, thegoals and actions model 648, the narrative controller 650, and theanimation and gesture model 652 provide client-side narrative triggers656 and animation controls 658. The dialogue output 654, the client-sidenarrative triggers 656, and the animation controls 658 provide thedialogue, the events, the client-side triggers, and the animations thatneed to be enacted on the client side.

The dialogue output 654, the client-side narrative triggers 656, theanimation controls 658, and the voice parameters 644 may be processedusing text to speech conversion 660. The output data obtained uponapplying the text to speech conversion 660 are sent as a stream to theclient 662. The game engine animates the AI character based on thereceived data to provide the generative behavior of the AI character.The animating may include, for example, instructing the AI character onwhat to say, how to move, what to enact, and the like.

FIG. 7A and FIG. 7B show an architecture diagram 700 illustrating AIcharacter models with goal-oriented behavior, according to an exampleembodiment. The AI character models may include generative modelsconfigured to follow sequential instructions for dialog and actions thatare driven by a specific purpose or intent for AI-driven characters.FIG. 7A shows possible user inputs 702 and input impact for goals model704. The possible user inputs 702 include fields that are exposed to theuser and can be changed by the user in the studio. The input impact forgoals model 704 includes impacts of each user input on the goals model.

Compared to general language models that provide general goals for AIcharacters, the goals model enables providing specific goals. FIG. 7Ashows that each type of configuration caused by the possible user inputs702 may influence the goals and actions of the AI character. Morespecifically, the AI character personality and background description706 selected by the user has an impact on the constitution of AIcharacter personality and style, which biases the reason for which, andmanner in which, the AI character pursues goals, as shown in block 708.Therefore, the AI character personality and background description 706may influence how the AI character enacts its goals. For example, if theAI characters are Alice in Wonderland versus Jack Sparrow, the AIcharacters may have the exact same goal (e.g., to show their house to aplayer). However, the AI characters may show their houses in completelydifferent ways because the AI characters represent two different people.

The motivations 710 received from the user may structure top-levelmotivations that underlie the reasoning for all AI character behaviorand directions, as shown in block 712. Therefore, the motivations 710may effectively determine why this AI character is pursuing this goal,i.e., determine the top-level motivation of the AI character. Forexample, the motivation of Alice in Wonderland is to get home. The goalsof Alice are to ask the Mad Hatter what he knows about Wonderland. Thesegoals may be determined and provided to the top-level motivation.

Flaws and challenges 714 selected by the user allow establishment offlaws and challenges for the AI character, which may influence,motivate, or hinder goal enactment by the AI character, as shown inblock 716.

An identity profile 718 selected by the user may specify elements of anAI character (e.g., role, interests) which may have an influence on howthe AI character pursues goals (e.g., a policeman trying to uncoverinformation differently from a salesperson), as shown in block 720. Theflaws and challenges 714 and the identity profile 718 are ways ofenacting so as to influence the goal more contextually. For example, theAI character is Indiana Jones and his flaw is that he is scared ofsnakes. The goal of the AI character is to cross a cavern covered insnakes. Therefore, based on the flaw, the AI character may say, “Oh, I'mso scared of snakes,” and then achieve the goal. Therefore, the flawsand challenges 714 are used to add a context to the goal-orientedbehavior of the AI character. The identity profile 718 is used similarlyto further contextualize the goal-oriented behavior of the AI character.For example, the AI characters may include a police person (a firstidentity) and a salesperson (a second identity) both trying to uncoverinformation, but the salesperson may do it very differently than thepolice person.

An emotional profile 722 received from the user may be used to establishan emotional profile of an AI character, such that the emotional profilemay influence expression of goals, as shown in block 724. The emotionalprofile 722 may include the expression. For example, the introvertednessof the AI character may be turned up to make the AI characterintroverted, in which case if the AI character had to sell something orthe AI character had to say something to someone, the AI character maybe more nervous than if the AI character was extroverted.

Various parts of memories, such as a personal memory 726, worldknowledge 730, and contextual knowledge 734 provide information that maybe relevant to the pursuit of a goal. Specifically, the personal memory726 may be used to provide an AI character with personal memories thatmay be brought up during the pursuit of a goal, as shown in block 728.For example, if the AI character remembers that the AI characterrecently was bitten by a dog and the goal is to go in and tie up a dog,the AI character may express fear or angst and say, “Oh, I can do that,but I'm really scared, I had this bad experience.” Therefore, changingthe behavior of the AI character based on the personal memory 726 makesthe behavior more realistic.

The world knowledge 730 may be used to integrate information about theworld to contextualize pursuit of the goal, as shown in block 732. Theworld knowledge 730 may be used to further contextualize the behavior ofthe AI character. For example, in a specific science fiction world, theAI character knows that all the police are corrupt in an area andworking for an evil overlord. Therefore, the AI character may be scaredor show more cautious when pursuing an investigation.

The contextual knowledge 734 may be processed to include informationabout an environment or context to contextualize pursuit of the goal, asshown in block 736. For example, if a volcano has just exploded and theAI character is asked to carry a girl to safety, the AI character mayshow more hurriedness, and may be forceful to the girl, versus if thatwas not true, the AI character might pursue the goal differently.

Voice configuration 738 may be used to determine the configuration ofvoice in real-time, which can allow AI characters to show differentexpressions when pursuing a goal, as shown in block 740. For example, ifthe AI character is a fireman who is saving someone, it may be extremelyloud in a burning building; therefore, the voice of the AI character maybe made loud and forceful. The AI character may pursue the goaldifferently as compared, for example, the case when the AI character wasdoing the same actions in a courtroom.

Dialogue style controls 742 may be used to control a dialogue style ofan AI character. The dialogue style may influence the manner and styleof speech of the AI character, as shown in block 744. For example, theuser may set the dialog style to be a modern day New York dialogue styleor a Wild West style. In each of the styles, the AI character may usedifferent words. For example, a Wild West bartender may use slang whenselling a drink.

Goals and actions 746 received from the user may be processed to specifythe goals that an AI character has per scene, and then set up theactions that the AI character has available to pursue the goal, as shownin block 748. Therefore, the goals and actions 746 specify the goals forthe scene in which the AI character is currently present, the sequenceof goals, and actions that the AI characters have to do to pursue thegoals.

Animation triggers and controls 750 may include animations and gestures,which may determine which actual physical movements the AI character cantake to pursue the goal, as shown in block 752. For example, the AIcharacter is selling an item and needs to take the item off the shelfand show it to the player when selling.

The input impact for goals model 704 are provided to a plurality of AImodels to generate a consequent behavior 754 due to goal configurations,as shown in FIG. 7B. More specifically, the LLM may determine what theAI character needs to say to enact the goal, as shown in block 756. Thegoals and actions model shown in block 758 is the controller fordetermining which goals need to be pursued and in which order, when isthe goal confirmed as complete, and the like.

The narrative controller determines how the narrative progressesdepending on how the AI character pursues the goal (the goal issuccessful or failed) and if the narrative shifts as a result of asuccess or a failure, as shown in block 760. For example, in a game anAI character is supposed to save a girl, but the AI character fails, andthe girl dies. This failure to complete the goal may change thenarrative. The narrative controller may send a trigger to change thebehavior of the AI character based on this failure to the game engine.

The text to speech conversion model determines how the AI characterspeaks his lines (audio) to pursue the goal, as shown in block 762. Theparameters to be changed may also include, for example, the dialoguestyle and voice configuration.

The animation and gesture model may determine what actual actions,animations, or gestures the AI character enacts to pursue the goal(e.g., smiling and taking an item off the shelf, picking up a girl tosave her from a burning building), as shown in block 764.

The outputs obtained in blocks 756-764 may include a dialogue output(audio or text) 766, client side narrative triggers 768, and animationcontrols 770. The dialogue output (audio or text) 766, the client sidenarrative triggers 768, and the animation controls 770 may be providedto a client 772 (e.g., a client engine, a game engine, a webapplication, and the like).

FIG. 8 is a block diagram 800 illustrating a narrative structure thatshows a context of scenes used to distinguish context for goals,according to an example embodiment. The narrative structure may includeworld/narrative settings 802 and world knowledge 804 (world knowledgefor all AI characters in all scenes). The world/narrative settings 802and the world knowledge 804 may be used to transition from one scene toanother in a story. Therefore, a story or an experience associated withan AI character may happen as a series of scenes and transitions.

In an example embodiment, an AI character may exist in a scene 806.Based on the world/narrative settings 802 and the world knowledge 804,the scene 806 may be transitioned in block 808 into a scene 810 and ascene 812. The scene 810 may be transitioned in block 814 and the scene812 may be transitioned in block 816 into a scene 818, a scene 820, anda scene 822.

FIG. 9 is a block diagram 900 illustrating a structure of goals withinscenes, according to an example embodiment. Within each scene, for eachspecific AI character, there is a goal that the AI character has topursue. A scene 902 may be driven by a plurality of parameters. Theparameters may include scene and location knowledge 904, which mayinclude world knowledge for all AI characters. The parameters mayfurther include historical knowledge 906, which may include knowledgefrom previous scenes and from transition between the previous scene andthe current scene 902. The parameters may further include relationships908, which determine relations between AI characters 910, 920, and 922.Each of the AI characters 910, 920, and 922 may have contextualknowledge 912, i.e., scene-specific knowledge. Each of the AI characters910, 920, and 922 may further have a goal set 914. The goal set 914 mayinclude a plurality of goals 916. Each of the goals 916 may beassociated with a plurality of actions 918 to be taken by the AIcharacter to pursue the goals 916.

In an example embodiment, scene 902 is a scene in which the AI character910 is Indiana Jones who enters a cave (scene and location knowledge904). The context is as follows: the AI character 910 knows that he isscared of snakes (contextual knowledge 912), but he is running away fromenemies (contextual knowledge 912) and the AI character 910 now has thefirst goal 916 to run through the cave and escape the snakes. Therefore,the AI character 910 has actions 918 available to pursue the goal 916.The actions 918 may include running, asking for help, and the like. Thenext goal 916 of the AI character 910 may be to find the buriedtreasure. The last goal 916 may be to escape. For each of those goals916, the AI character 910 has specific actions 918 that are availablefor the AI character 910 to pursue.

FIG. 10 is a flow chart of a method 1000 for observation-based trainingof an AI character model, according to an example embodiment. In someembodiments, the operations may be combined, performed in parallel, orperformed in a different order. The method 1000 may also includeadditional or fewer operations than those illustrated. The method 1000may be performed by processing logic that may comprise hardware (e.g.,decision making logic, dedicated logic, programmable logic, andmicrocode), software (such as software run on a general-purpose computersystem or a dedicated machine), or a combination of both. The method1000 may be implemented with a processor of a computing platform forobservation-based training of an AI character model.

The method 1000 may commence in block 1002 with receiving, by theprocessor, log data. The log data may include interactions of a firstuser and a second user (i.e., user-to-user interactions), interactionsof the first user and an AI character (i.e., character-to-userinteractions), and so forth. The interactions may be performed in avirtual environment, also referred to as a computer-based environment.The interactions may include movements of users or AI characters,gestures of users or AI characters, actions taken by users or AIcharacters, emotions shown by users or AI characters, conversations ofusers or AI characters in the virtual environment, and so forth.

In some example embodiments, the log data may include a recording of averbal conversation between the first user and the second user. In anexample embodiment, the log data may include a description of a scene inthe computer-based environment. The computer-based environment mayinclude, for example, a computer game.

The user may include a developer of the AI character model, a playerthat interacts with an AI character in a virtual environment, and soforth. In some example embodiments, the log data to be used by the AIcharacter model for learning may be preselected by a developer of an AIcharacter, by a user that interacts with an AI character and wants toset the parameters of the AI character, and so forth.

In block 1004, the method 1000 may include adjusting, by the processorand based on the log data, parameters of the AI character model to causethe AI character model to mimic behavioral characteristics of the firstuser in follow-up conversations with further users. The AI charactermodel may include an LLM or a generative model and may be configured tolearn to mimic any behavior (e.g., behavior of a user or an AI characterin a virtual environment) observed by the AI character model. The AIcharacter model may determine the behavioral characteristics of thefirst user based on the log data. Accordingly, the log data may be usedto teach the AI character model to modify the parameters of the AIcharacter to make the AI character to have the behavioralcharacteristics observed by the AI character model. As the AI charactermodel observes multiple behavioral characteristics of the first user,the AI character model may learn to adjust multiple parameters of the AIcharacter at once. For example, the parameters of the AI character modelto be modified may include one or more of the following: a role, anintent, knowledge, an emotional state of an AI character (e.g.,friendly, unfriendly, neutral, and the like), a type of personality(sanguine, choleric, melancholic, and phlegmatic), responses inconversations in which the AI character participates, and so forth.

In an example embodiment, the adjustment of the parameters of the AIcharacter model may include modifying internal parameters correspondingto a role of the AI character model to cause the role to match a furtherrole of the first user in the interactions.

In some example embodiments, the adjustment of the parameters of the AIcharacter model may include modifying internal parameters correspondingto an intent of the AI character model to cause the intent to match afurther intent of the first user in the interactions.

In an example embodiment, the adjustment of the parameters of the AIcharacter model may include modifying internal parameters correspondingto an emotional state of the AI character model to cause the emotionalstate to match a further emotional state of the first user in theinteractions.

In some example embodiments, the adjustment of the parameters of the AIcharacter model may include modifying internal parameters for generatinga response of the AI character model to a specific message of the seconduser to cause the response to match a further response of the first userto the specific message of the second user in the interactions.

In an example embodiment, the adjustment of the parameters of the AIcharacter model may include modifying internal parameters for generatingan action of the AI character model in response to a specific message ora user action of the second user to cause the action to match a furtheraction of the first user in a further response to the specific messageor the user action of the second user in the interactions. The actionmay include one of the following: a gesture, a movement of the AIcharacter model, and so forth.

In the embodiment in which the log data includes the recording of theverbal conversation between the first user and the second user, theadjustment of the parameters of the AI character model may includemodifying internal parameters corresponding to voice characteristics ofthe AI character model, thereby causing the voice characteristics tomatch further voice characteristics of the first user in theinteractions.

The method 1000 may optionally include generating, by the processor,further log data including further interactions between the AI charactermodel and the second user. The further interactions may include messagesof the second user in the log data and responses to the messages. Theresponses may be generated by the AI character model. Upon generatingthe further log data, method 1000 may proceed with comparing, by theprocessor, the log data and the further log data to obtain discrepanciesbetween the responses generated by the AI character model andcorresponding responses of the first user in the log data. The method1000 may proceed with performing, by the processor and based on thediscrepancies, further adjustments of parameters of the AI charactermodel.

In an example embodiment, the AI character model may generate AIcharacters that are representatives of different professions. As anexample, a user may build an AI character that is a virtual Starbucks®barista. A real human actor may be involved in the environment to playthe role of a barista. The interactions of the real human actor can berecorded in log data. The log data can be used to train the AI charactermodel. Thus, the learning of the AI character model may be performed bythe AI character model “observing the actor,” thereby trying to mimicthe whole experience of “the actor.” The learning of the AI charactermodel may include a feedback loop that receives the results showingwhether the AI character model works. Based on the results, the behaviorof the AI character generated by the AI character model may be adjusted.

The log data may be taken from any existing information sources, such asmovies, books, comics, and so forth. In an example embodiment, a usermay provide a transcript of a movie (e.g., Alice in Wonderland) to acomputing platform for training an AI character model and instruct thecomputing platform to adjust the AI character model to behave more likecharacters of the movie. The computing platform may use the transcriptas the log data, determine the behavioral characteristics (emotions,vocabulary, gestures, knowledge of facts and skills, etc.) of thecharacters of the movie based on the transcript, and adjust theparameters of the AI character model to behave in the virtualenvironment similarly to the characters of the movie.

In an example embodiment, a user may have an interaction with an AIcharacter (e.g., Alice in Wonderland) in a virtual world environment andselect the interaction as a desired interaction. The user may providethe log data that represent the desired interactions and feed the logdata to a computing platform for training an AI character model. Thecomputing platform may use the log data to adjust the parameters of theAI character model to have the behavior similar to the behavior of theuser or the AI character in the log data.

In an example embodiment, the log data may be provided to the computingplatform for training the AI character model in a form of an exampledialog between characters. For example, the user may be creating an AIcharacter that is Captain Jack Sparrow and may upload a transcript fromthe Pirates of the Caribbean® movie. The computing platform may use thetranscript as the log data and bias the AI character model towardsbehaving like Captain Jack Sparrow.

In another example embodiment, the user may be creating an AI characterthat is Buzz Lightyear. The AI character model may include a genericmodel. The user may use every transcript that Buzz Lightyear has eversaid across every movie and any other source to teach the AI charactermodel to modify the parameters of the AI character model so as to createan AI character that replicates behavioral characteristics of BuzzLightyear.

FIG. 11 is a high-level block diagram illustrating an example computersystem 1100, within which a set of instructions for causing the machineto perform any one or more of the methodologies discussed herein can beexecuted. The computer system 1100 may include, refer to, or be anintegral part of, one or more of a variety of types of devices, such asa general-purpose computer, a desktop computer, a laptop computer, atablet computer, a netbook, a mobile phone, a smartphone, a personaldigital computer, a smart television device, and a server, among others.Notably, FIG. 11 illustrates just one example of the computer system1100 and, in some embodiments, the computer system 1100 may have fewerelements/modules than shown in FIG. 11 or more elements/modules thanshown in FIG. 11 .

The computer system 1100 may include one or more processor(s) 1102, amemory 1104, one or more mass storage devices 1106, one or more inputdevices 1108, one or more output devices 1110, and a network interface1112. The processor(s) 1102 are, in some examples, configured toimplement functionality and/or process instructions for execution withinthe computer system 1100. For example, the processor(s) 1102 may processinstructions stored in the memory 1104 and/or instructions stored on themass storage devices 1106. Such instructions may include components ofan operating system 1114 or software applications 1116. The softwareapplications may include the studio 204, the integration interface 206,and the AI character model 202. The computer system 1100 may alsoinclude one or more additional components not shown in FIG. 11 , such asa housing, a power supply, a battery, a global positioning system (GPS)receiver, and so forth.

The memory 1104, according to one example, is configured to storeinformation within the computer system 1100 during operation. The memory1104, in some example embodiments, may refer to a non-transitorycomputer-readable storage medium or a computer-readable storage device.In some examples, the memory 1104 is a temporary memory, meaning that aprimary purpose of the memory 1104 may not be long-term storage. Thememory 1104 may also refer to a volatile memory, meaning that the memory1104 does not maintain stored contents when the memory 1104 is notreceiving power. Examples of volatile memories include random accessmemories (RAM), dynamic random access memories (DRAM), static randomaccess memories (SRAM), and other forms of volatile memories known inthe art. In some examples, the memory 1104 is used to store programinstructions for execution by the processor(s) 1102. The memory 1104, inone example, is used by software (e.g., the operating system 1114 or thesoftware applications 1116). Generally, the software applications 1116refer to software applications suitable for implementing at least someoperations of the methods for observation-based training of an AIcharacter model as described herein.

The mass storage devices 1106 may include one or more transitory ornon-transitory computer-readable storage media and/or computer-readablestorage devices. In some embodiments, the mass storage devices 1106 maybe configured to store greater amounts of information than the memory1104. The mass storage devices 1106 may further be configured forlong-term storage of information. In some examples, the mass storagedevices 1106 include non-volatile storage elements. Examples of suchnon-volatile storage elements include magnetic hard disks, opticaldiscs, solid-state discs, flash memories, forms of electricallyprogrammable memories (EPROM) or electrically erasable and programmablememories, and other forms of non-volatile memories known in the art.

The input devices 1108, in some examples, may be configured to receiveinput from a user through tactile, audio, video, or biometric channels.Examples of the input devices 1108 may include a keyboard, a keypad, amouse, a trackball, a touchscreen, a touchpad, a microphone, one or morevideo cameras, image sensors, fingerprint sensors, or any other devicecapable of detecting an input from a user or other source, and relayingthe input to the computer system 1100, or components thereof.

The output devices 1110, in some examples, may be configured to provideoutput to a user through visual or auditory channels. The output devices1110 may include a video graphics adapter card, a liquid crystal display(LCD) monitor, a light emitting diode (LED) monitor, an organic LEDmonitor, a sound card, a speaker, a lighting device, a LED, a projector,or any other device capable of generating output that may beintelligible to a user. The output devices 1110 may also include atouchscreen, a presence-sensitive display, or other input/output capabledisplays known in the art.

The network interface 1112 of the computer system 1100, in some exampleembodiments, can be utilized to communicate with external devices viaone or more data networks such as one or more wired, wireless, oroptical networks including, for example, the Internet, intranet, LAN,WAN, cellular phone networks, Bluetooth radio, and an IEEE 902.11-basedradio frequency network, Wi-Fi networks®, among others. The networkinterface 1112 may be a network interface card, such as an Ethernetcard, an optical transceiver, a radio frequency transceiver, or anyother type of device that can send and receive information.

The operating system 1114 may control one or more functionalities of thecomputer system 1100 and/or components thereof. For example, theoperating system 1114 may interact with the software applications 1116and may facilitate one or more interactions between the softwareapplications 1116 and components of the computer system 1100. As shownin FIG. 11 , the operating system 1114 may interact with or be otherwisecoupled to the software applications 1116 and components thereof. Insome embodiments, the software applications 1116 may be included in theoperating system 1114. In these and other examples, virtual modules,firmware, or software may be part of the software applications 1116.

Thus, systems and methods for observation-based training of an AIcharacter model have been described. Although embodiments have beendescribed with reference to specific example embodiments, it will beevident that various modifications and changes can be made to theseexample embodiments without departing from the broader spirit and scopeof the present application. Accordingly, the specification and drawingsare to be regarded in an illustrative rather than a restrictive sense.

What is claimed is:
 1. A method for observation-based training of anArtificial Intelligence (AI) character model, the method beingimplemented with a processor of a computing platform providing the AIcharacter model, the method comprising: receiving, by the processor, logdata including interactions of a first user and a second user; andadjusting, by the processor and based on the log data, parameters of theAI character model to cause the AI character model to mimic behavioralcharacteristics of the first user in follow-up conversations withfurther users, wherein: the AI character model includes a firstplurality of heuristic machine learning models and a second plurality ofprimary machine learning models, the first plurality of heuristicmachine learning models including an events model, an intent recognitionmodel, and an emotion model, and the second plurality of primary machinelearning models including a large language model, a goals and actionsmodel, and an animation and gesture model; and the adjusting theparameters of the AI character model includes: pre-processing the logdata to obtain a plurality of streams, the plurality of streamsincluding a first stream of text pronounced by the first user during theinteractions, a second stream of actions performed by the first userduring the interactions, and a third stream of events occurring duringthe interactions; running the plurality of streams through the firstplurality of heuristic machine learning models to produce intermediateoutputs; composing the intermediate outputs into templated formats forthe second plurality of primary machine learning models; and feeding thetemplated formats to the second plurality of primary machine learningmodels.
 2. The method of claim 1, wherein the adjusting the parametersof the AI character model includes modifying internal parameterscorresponding to a role of the AI character model to cause the role tomatch a further role of the first user in the interactions.
 3. Themethod of claim 1, wherein the adjusting the parameters of the AIcharacter model includes modifying internal parameters corresponding toan intent of the AI character model to cause the intent to match afurther intent of the first user in the interactions.
 4. The method ofclaim 1, wherein the adjusting the parameters of the AI character modelincludes modifying internal parameters corresponding to an emotionalstate of the AI character model to cause the emotional state to match afurther emotional state of the first user in the interactions.
 5. Themethod of claim 1, wherein the adjusting the parameters of the AIcharacter model includes modifying internal parameters for generating aresponse of the AI character model to a specific message of the seconduser to cause the response to match a further response of the first userto the specific message of the second user in the interactions.
 6. Themethod of claim 1, wherein the adjusting the parameters of the AIcharacter model includes modifying internal parameters for generating anaction of the AI character model in response to a specific message or auser action of the second user to cause the action to match a furtheraction of the first user in a further response to the specific messageor the user action of the second user in the interactions.
 7. The methodof claim 6, wherein the action includes one of the following: a gestureand a movement of the AI character model.
 8. The method of claim 1,wherein: the log data includes a recording of a verbal conversationbetween the first user and the second user; and adjusting the parametersof the AI character model includes modifying internal parameterscorresponding to voice characteristics of the AI character model,thereby causing the voice characteristics to match further voicecharacteristics of the first user in the interactions.
 9. The method ofclaim 1, further comprising: generating, by the processor, further logdata including further interactions between the AI character model andthe second user, the further interactions including messages of thesecond user in the log data and responses to the messages, the responsesbeing generated by the AI character model; comparing, by the processor,the log data and the further log data to obtain discrepancies betweenthe responses generated by the AI character model and correspondingresponses of the first user in the log data; and performing, by theprocessor and based on the discrepancies, further adjustments ofparameters of the AI character model.
 10. The method of claim 1, whereinthe log data includes a description of a scene in a computer-basedenvironment.
 11. A computing platform for observation-based training ofan Artificial Intelligence (AI) character model, the computing platformcomprising: a processor; and a memory storing instructions that, whenexecuted by the processor, configure the computing platform to: receivelog data including interactions of a first user and a second user; andadjust, based on the log data, parameters of the AI character model tocause the AI character model to mimic behavioral characteristics of thefirst user in follow-up conversations with further users, wherein: theAI character model includes a first plurality of heuristic machinelearning models and a second plurality of primary machine learningmodels, the first plurality of heuristic machine learning modelsincluding an events model, an intent recognition model, and an emotionmodel, and the second plurality of primary machine learning modelsincluding a large language model, a goals and actions model, and ananimation and gesture model; and the adjusting the parameters of the AIcharacter model includes: pre-processing the log data to obtain aplurality of streams, the plurality of streams including a first streamof text pronounced by the first user during the interactions, a secondstream of actions performed by the first user during the interactions,and a third stream of events occurring during the interactions; runningthe plurality of streams through the first plurality of heuristicmachine learning models to produce intermediate outputs; composing theintermediate outputs into templated formats for the second plurality ofprimary machine learning models; and feeding the templated formats tothe second plurality of primary machine learning models.
 12. Thecomputing platform of claim 11, wherein the adjusting the parameters ofthe AI character model includes modifying internal parameterscorresponding to a role of the AI character model to cause the role tomatch a further role of the first user in the interactions.
 13. Thecomputing platform of claim 11, wherein the adjusting the parameters ofthe AI character model includes modifying internal parameterscorresponding to an intent of the AI character model to cause the intentto match a further intent of the first user in the interactions.
 14. Thecomputing platform of claim 11, wherein the adjusting the parameters ofthe AI character model includes modifying internal parameterscorresponding to an emotional state of the AI character model to causethe emotional state to match a further emotional state of the first userin the interactions.
 15. The computing platform of claim 11, wherein theadjusting the parameters of the AI character model includes modifyinginternal parameters for generating a response of the AI character modelto a specific message of the second user to cause the response to matcha further response of the first user to the specific message of thesecond user in the interactions.
 16. The computing platform of claim 11,wherein the adjusting the parameters of the AI character model includesmodifying internal parameters for generating an action of the AIcharacter model in a response to a specific message or a user action ofthe second user to cause the action to match a further action of thefirst user in a further response to the specific message or the useraction of the second user in the interactions.
 17. The computingplatform of claim 16, wherein the action includes one of the following:a gesture and a movement of the AI character model.
 18. The computingplatform of claim 11, wherein: the log data includes a recording of averbal conversation between the first user and the second user; and theadjusting the parameters of the AI character model includes modifyinginternal parameters corresponding to voice characteristics of the AIcharacter model, thereby causing the voice characteristics to matchfurther voice characteristics of the first user in the interactions. 19.The computing platform of claim 11, wherein the instructions furtherconfigure the computing platform to: generate further log data includingfurther interactions between the AI character model and the second user,the further interactions including messages of the second user in thelog data and responses to the messages, the responses being generated bythe AI character model; compare the log data and the further log data toobtain discrepancies between the responses generated by the AI charactermodel and corresponding responses of the first user in the log data; andperform, based on the discrepancies, further adjustments of parametersof the AI character model.
 20. A non-transitory computer-readablestorage medium, the computer-readable storage medium includinginstructions that, when executed by a processor of a computing platformfor observation-based training of an Artificial Intelligence (AI)character model, cause the computing platform to: receive log dataincluding interactions of a first user and a second user; and adjust,based on the log data, parameters of the AI character model to cause theAI character model to mimic behavioral characteristics of the first userin follow-up conversations with further users, wherein: the AI charactermodel includes a first plurality of heuristic machine learning modelsand a second plurality of primary machine learning models, the firstplurality of heuristic machine learning models including an eventsmodel, an intent recognition model, and an emotion model, and the secondplurality of primary machine learning models including a large languagemodel, a goals and actions model, and an animation and gesture model;and the adjusting the parameters of the AI character model includes:pre-processing the log data to obtain a plurality of streams, theplurality of streams including a first stream of text pronounced by thefirst user during the interactions, a second stream of actions performedby the first user during the interactions, and a third stream of eventsoccurring during the interactions; running the plurality of streamsthrough the first plurality of heuristic machine learning models toproduce intermediate outputs; composing the intermediate outputs intotemplated formats for the second plurality of primary machine learningmodels; and feeding the templated formats to the second plurality ofprimary machine learning models.